code: def _learn_from_batch(self, batch):
      fetches = [
          self.networks["main"]
          .online_network.output_heads[1]
          .probability_loss,
          self.networks["main"]
          .online_network.output_heads[1]
          .bias_correction_loss,
          self.networks["main"]
          .online_network.output_heads[1]
          .kl_divergence,
      ]
  
      # batch contains a list of transitions to learn from
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # get the values for the current states
      Q_values, policy_prob = self.networks[
          "main"
      ].online_network.predict(batch.states(network_keys))
      avg_policy_prob = self.networks["main"].target_network.predict(
          batch.states(network_keys)
      )[1]
      current_state_values = np.sum(policy_prob * Q_values, axis=1)
  
      actions = batch.actions()
      num_transitions = batch.size
      Q_head_targets = Q_values
  
      Q_i = Q_values[np.arange(num_transitions), actions]
  
      mu = batch.info("all_action_probabilities")
      rho = policy_prob / (mu + eps)
      rho_i = rho[np.arange(batch.size), actions]
  
      rho_bar = np.minimum(1.0, rho_i)
  
      if batch.game_overs()[-1]:
          Qret = 0
      else:
          result = self.networks["main"].online_network.predict(
              last_sample(batch.next_states(network_keys))
          )
          Qret = np.sum(result[0] * result[1], axis=1)[0]
  
      for i in reversed(range(num_transitions)):
          Qret = batch.rewards()[i] + self.ap.algorithm.discount * Qret
          Q_head_targets[i, actions[i]] = Qret
          Qret = rho_bar[i] * (Qret - Q_i[i]) + current_state_values[i]
  
      Q_retrace = Q_head_targets[np.arange(num_transitions), actions]
  
      # train
      result = self.networks["main"].train_and_sync_networks(
          {
              **batch.states(network_keys),
              "output_1_0": actions,
              "output_1_1": rho,
              "output_1_2": rho_i,
              "output_1_3": Q_values,
              "output_1_4": Q_retrace,
              "output_1_5": avg_policy_prob,
          },
          [Q_head_targets, current_state_values],
          additional_fetches=fetches,
      )
  
      for network in self.networks.values():
          network.update_target_network(
              self.ap.algorithm.rate_for_copying_weights_to_target
          )
  
      # logging
      total_loss, losses, unclipped_grads, fetch_result = result[:4]
      self.q_loss.add_sample(losses[0])
      self.policy_loss.add_sample(losses[1])
      self.probability_loss.add_sample(fetch_result[0])
      self.bias_correction_loss.add_sample(fetch_result[1])
      self.unclipped_grads.add_sample(unclipped_grads)
      self.V_Values.add_sample(current_state_values)
      self.kl_divergence.add_sample(fetch_result[2])
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import Batch
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace
  from rl_coach.utils import eps, last_sample
label_desc: Use image node_id 3 for calling the ACERAgent obj's underlying member method code with example usage: obj._learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads